Private equity (PE) firms devote significant efforts to refining investment theses, capital structures, and value creation plans. But a PE firm’s value creation plan is only as strong as the financial infrastructure beneath it. Having financial planning and analysis (FP&A) capabilities that can’t match PE-grade operational demands can create bottlenecks in achieving growth targets.

This is often an infrastructure problem. Some sponsors, particularly in the lower and middle-market, underinvest in finance function buildout during the first twelve months of ownership (precisely when the cost of bad data is highest). The consequence is delayed variance identification, unreliable KPI reporting, and FP&A that functions as a backward-looking scorekeeping exercise rather than the forward-looking financial intelligence function.

The following article discusses how to develop a phased, deliberate buildout of real FP&A capability, whether through fractional resources, in-house talent, or a hybrid model.

FP&A in PE Is Different

PE-backed FP&A operates fundamentally differently from its corporate counterpart. It must simultaneously serve management’s operational cadence and the sponsor’s IRR thesis, reconciling GAAP actuals with quality of earnings-adjusted metrics and translating a revised revenue trajectory into hold-period return implications in near real time.

The shift happening across the PE landscape right now reinforces this point. With compressed multiples, elevated rates, and extended hold periods pushing sponsors to deliver annual EBITDA growth north of 4% to hit target IRR thresholds,¹ the pressure on FP&A to generate actionable intelligence has grown. The best FP&A functions in PE today are characterized by KPIs tightly integrated with operational drivers, genuine forward-looking orientation, and the ability to model strategic scenarios in near real time.

FP&A capability relies on three integrated pillars: a clean and governed data architecture; a dynamic, driver-based planning model; and a structured analytical cadence that connects operational leading indicators to financial outcomes. Most Excel-dependent portfolio companies have fragments of all these pillars fragmented across disconnected workbooks with no version control, no audit trail, and no single source of truth. Phase 1 of building an FP&A strategy involves solving this and establishing the foundation on which everything else depends.

Phase One: Stabilizing and Diagnosing

The key objective of the first phase is data integrity and process cartography. The first phase involves a forensic audit of how numbers are currently produced as well as mapping every data handoff between source systems such as ERP, CRM, billing platform, payroll system, and the management reporting layer. Most mid‑market finance teams still rely on multiple manual spreadsheet handoffs and offline calculations during the monthly closing. Every manual touchpoint becomes an error‑introduction risk that is hard to detect in the final reports. It is important to document the owner of each step, the frequency of execution, and the validation controls (if any) in place.

Simultaneously, this phase will include a KPI audit. Every portfolio company has metrics the board cares about, such as ARR, gross margin, customer acquisition cost, net revenue retention, working capital turns, and covenant compliance ratios. For each, it is important to trace the calculation back to its source data. If the path from system of record to reported metric passes through a manually maintained spreadsheet with no version history, that metric is operationally unreliable. Such issues should be flagged, magnitude of the risk should be quantified, and a remediation priority matrix should be built.

The urgency of clean data architecture is further reinforced by ASC 842, the GAAP lease accounting standard that requires companies to recognize most lease obligations as right-of-use assets and liabilities on the balance sheet.² For mid-market portfolio companies with significant real estate or equipment lease exposure, failure to maintain governed lease data directly distorts metrics such as reported EBITDA and leverage ratios.

This is where fractional FP&A resources deliver outsized ROI. Having completed this phase across dozens of portfolio companies, seasoned fractional CFOs or senior FP&A professionals bring pattern recognition that compresses the diagnostic timeline considerably. They also understand which control gaps to prioritize and which vendor integrations are technically feasible within the existing ERP environment.

By the end of phase one, the PE-backed company will have a documented data flow map, a prioritized list of manual processes with the highest error risk, and executive alignment on the KPIs that genuinely matter to the investment thesis.

Phase Two: Building the Foundation

The second phase is focused on infrastructure, migrating the planning function from a static, calendar-driven Excel exercise to a dynamic, driver-based model that reflects how the business generates and consumes value.

Driver-based modeling is the practice of building financial forecasts from operational assumptions such as units sold, headcount by function, utilization rates, churn, and pricing, rather than extrapolating from prior-period actuals. This distinction becomes even more important in a PE context because it shifts the variance conversation from “what happened to the number” to “what happened to the driver that moved the number.” The latter is a fundamentally more actionable frame for both management and the board.

The model should be structured in three discrete layers. A data layer will connect to source systems via clean extracts or API integrations, and an assumptions layer that will capture the key operational drivers for each business unit. Finally, an output layer will feed the consolidated P&L, balance sheet, and cash flow statement as well as scenario analysis outputs. Maintaining strict separation between these layers is what prevents the model from becoming the opaque tangle of hardcoded cells and circular references that plagues most inherited Excel-based planning environments.

Alongside the model, the second phase will involve scoping and implementing the reporting infrastructure. For many portfolio companies below $100M in revenue, a purpose-built FP&A platform or a well-architected environment sitting on top of the ERP will dramatically reduce manual close work. They can also enable the kind of real-time dashboard visibility that sponsors increasingly require for cash flow, working capital, and KPI tracking. The goal is a monthly board package that is largely automated by end of phase, with finance spending their time on analysis rather than assembly.

Phase Three: Operationalizing

Phase three is about turning FP&A from a reporting function into the strategic engine of the business. The goal is to use the insights generated from FP&A infrastructure to improve capital allocation, hiring decisions, and strategic prioritization.

The monthly close review should be a structured, time-boxed session in which finance presents actual versus plan with clean variance analysis and explains the operational drivers behind the key variances. The commentary and discussion can point to a path forward.

The rolling twelve-month forecast is what structurally differentiates data-driven operations from organizations that rely primarily on historical reporting. An annual budget is typically set once, then defended long after underlying assumptions have changed. By midyear, it may reflect a world that no longer exists. A rolling forecast, however, provides a mechanism for continuous recalibration.

The annual operating plan, done correctly, is the investment thesis translated into operational and financial milestones. The best annual plans start with the sponsor’s exit model (what EBITDA multiple, on what revenue base, at what margin profile, in what timeframe, etc.), and work backward through the strategic initiatives, headcount build, and capital expenditure required to arrive at that outcome.

Every significant line item in the plan should be connected to a strategic objective that management can articulate and defend. Plans that lack this logic are indistinguishable from incremental budgets and provide no real basis for resource allocation decisions or mid-year course corrections.

Phase Four: Scaling and Deepening

By phase four, a company should have reliable KPIs with defensible methodology, a live driver-based model, and an operating cadence that keeps leadership and the sponsor aligned on a continuous basis. This phase is about extending the function’s reach and sophistication.

This is when business partnering becomes a formal operating model, embedding FP&A support directly within commercial, operational, and product functions so that business leaders have a finance partner who understands their unit economics and can quantify the financial implications of their decisions in real time. It is also when scenario modeling evolves from sensitivity tables to genuine strategic stress-testing.

For companies approaching a liquidity event, this phase is also when FP&A pivots into sell-side preparation mode. This means building the management case that will underpin the CIM and investor presentations, pressure-testing adjusted EBITDA add-backs against likely quality of earnings scrutiny, and stress-testing the forward revenue bridge against buyer due diligence assumptions. This phase will also ensure that the data architecture can withstand any level of forensic examination from credible financial buyer or growth equity investor. Companies that arrive at this stage with clean, auditable data infrastructure and a coherent FP&A narrative consistently command premium valuations.

The Bottom Line

For PE sponsors, FP&A is a core infrastructure for executing the investment thesis. When the finance function cannot support operational demands, growth initiatives stall.

A deliberate, phased buildout of FP&A capability changes that trajectory. It creates a forward-looking financial intelligence function that surfaces issues early and informs capital allocation decisions.

Sponsors that invest early in this infrastructure shorten the feedback loop between strategy and performance, leading to cleaner reporting and a higher probability of delivering on the value creation plan.

1. Scott M. Moss, Christopher J. Truitt, Daniel Wheadon, “Private Equity Mid-Year Trends in 2025,” Cherry Bekaert Insights, August 18, 2025.
2. Clive Nair, “Implementing ASC 842 Leases—Challenges and Benefits” Open Journal of Accounting, January 2024.